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Full text of "Development and validation of a growth model for Florida-grown soybeans with disease stress"

DEVELOPMENT AND VALIDATION OF A GROWTH MODEL FOR 
FLORIDA-GROWN SOYBEANS WITH DISEASE STRESS 



BY 

STEVEN BOYD JOHNSON 



DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF THE 
UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE 
REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY 



UNIVERSITY OF FLORIDA 
1982 



ACKNOWLEDGMENTS 



I wish to acknowledge the assistance of Dr. Richard 

D. Berger, major advisor for the studies and for the 
dissertation, who unselfishly contributed incalculable 
hours in these capacities. His dedication to science, 
his critical and independent thinking, and his approach 
to problems have earned my deepest respect. Special 
recognition for general assistance throughout the 
program of study and for critically reading and offering 
valuable suggestions on this dissertation is in order 
for the remainder of my supervisory committee: Drs. T. 

E. Freeman, H. H. Luke, and S. H. West. I would also 
like to recognize all the persons, too numerous to 
mention by name, that have in some way contributed to 
the completion of this d i s ser t a t aion . I also wish to 
acknowledge my wife, Jennifer, for she was the true 
sufferer through the long hours of the studies and 
preparation of the dissertation. The work was supported 
by funds from EPA grant no. CR- 806 27 7 -0 20-0 and research 
funds from Dr. Berger. 



ii 



1 



TABLE OF CONTENTS 



LIST OF TABLES v 

LIST OF FIGURES vii 

LIST OF ABBREVIATIONS ix 

ABSTRACT x 

GENERAL INTRODUCTION 1 

LITERATURE SURVEY 4 

I. DEVELOPMENT OF THE MODEL 8 

A. ) Introduction 8 

B. ) Materials and Methods 9 

1 .) Field Plots, 1980 12 

2.) Field Plots, 1981 12 

C. ) Results 13 

1 .) Field Plots, 1980 13 

2.) Field Plots, 1981 16 

D. ) Discussion 28 

1 . ) Field Plots , 1980 28 

2.) Field Plots, 1981 35 

II. DESCRIPTION OF THE MODEL 40 

A. ) Introduction 40 

B. ) Materials and Methods 41 

C . ) Results 43 

D.) Discussion 58 



ili 



III. VALIEATION AND VERIFICATION OF THE MODEL 67 

A. ) Introduction 67 

B. ) Materials and Methods 68 

C. ) Results 82 

D. ) Discussion 85 

IV. APPLICATIONS FOR THE MODEL 94 

A. ) Introduction 94 

B. ) Uses For The Model 96 

1. ) Foliar Pathogens 96 

2. ) Above-ground Non-foliar Pathogens 98 

3. ) Soil-borne Pathogens 103 

4. ) Foliage-feeding Insects 106 

5. ) Seed-feeding Insects Ill 

C. ) Results 115 

D. ) Discussion 117 

SUMMARY 121 

APPENDICES 123 

A: FUNGICIDES USED 123 

B: EQUIPMENT USED 124 

C: LEAF-AREA, DISEASE- AND INSECT-RATING SCALES... 125 

D: MATERIALS USED 142 

LITERATURE CITED 143 

BIOGRAPHICAL SKETCH 151 



iv 



LIST OF TABLES 



1. Soybean seed yields from plots, 1980 14 

2. Dry-matter yields from individually harvested 
soybean plants, 1980 15 

3. Pod and stem quality ratings from 

individually harvested soybean plants, 1980 17 

4. Soybean seed infection from plots, 1980 18 

5. Soybean seed infection from individually 
harvested plants, 1980 19 

6. Soybean seed yields from plots, 1981 20 

7. Dry-matter yields from individually 

harvested soybean plants, 1981 22 

8. Dry-matter yields from individually harvested 
soybean plants (analyzed with 

block three removed), 1981 23 

9. Pod and stem quality ratings from individually 
harvested soybean plants, 1981 25 

10. Soybean seed infection from plots, 1981 26 

11. Soybean seed infection from individually 
harvested plants, 1981 27 

12. Soybean seed yields from 

separate experiment, 1981 29 

13. Soybean pod and stem quality 

ratings from separate experiment, 1981 30 

14. Soybean seed infection from 

separate experiment, 1981 31 

15. Harvested above-ground plant dry matter 
proportioned into seeds, stems, and pods for 

two soybean cultivars, 1981 83 



v 



16. Published values, model predictions, and 

1980 and 1981 raw data for the same parameter s ... 84 

17. Harvested, above-ground soybean-plant dry-matter 
proportioned into seeds, stems, and pods 

for two seasons 86 

18. Final plant dry-matter ratios for two 

soybean cultivars, 1981 89 



vi 



LIST OF FIGURES 



1. The dry-matter accumulation for soybean 

leaves of known area over time 45 

2. The dry-matter accumulation for soybean 

stems over time 47 

3. The dry-matter accumulation for soybean 

seeds over time 49 

4. The dry-matter accumulation for soybean 

pods over time 51 

5. The dry-matter accumulation for soybean 

petioles over time 53 

6. The dry-matter accumulation for soybean 

leaves over time 55 

7. The dry-matter accumulation for entire 

soybean plants over time 57 

8. Flow chart of the soybean growth model 

(N = number of days; = rate of change) 61 

9. The dry-matter accumulation for soybean leaves, 
converted from leaf area (Fig. 1), 

from individually harvested 

plants over time, 1980 63 

10. The dry-matter accumulation for soybean leaves, 
converted from leaf area (Fig. 1), 

from individually harvested 

plants over time, 1981 65 

11. Model-predicted versus actual dry-matter 
accumulation for seeds, 1 980 70 

12. Model-predicted versus actual dry-matter 
accumulation for stems, 1980 72 

13. Model-predicted versus actual dry-matter 
accumulation for pods, 1980 74 

vii 



14. Model-predicted versus actual dry-matter 
accumulation for seeds, 1981 76 

15. Model-predicted versus actual dry-matter 
accumulation for stems, 1981 78 

16. Model-predicted versus actual dry-matter 
accumulation for pods, 1981 80 

17. Incorporation of a foliar-disease submodel 

into the soybean growth model (Fig. 8).... 100 

18. Incorporation of an above-ground, non-foliar 
disease submodel into the soybean growth 

model (Fig. 8) 105 

19. Incorporation of a soil-borne disease 
submodel into the soybean growth 

model (Fig. 8) 108 

20. Incorporation of a foliage-feeding insect 
submodel into the soybean growth 

model (Fig. 8) 110 

21. Incorporation of a seed-feeding insect 
submodel into the soybean growth 

model (Fig. 8) 114 



viii 



LIST OF ABBREVIATIONS 



In base e (natural) logarithm 

log base 10 logarithm 

m slope of linear regression equations 

R coefficient of determination 

r correlation coefficient 

To initial disease 

Ymax maximum disease level 

* multiplication 

** exponentat ion 

exp base of natural logarithm 

k epidemic rate for Gompertz transformation 

b (-ln{Yo}) in Gompertz transformation 

t t ime 

P probability level 

LSD least significant difference 

LAI leaf area index 

g gram 

ha hectare 

1 liter 



ix 



Abstract of Dissertation Presented to the Graduate 

Council of the University of Florida in Partial 
Fulfillment of the Requirements for the Degree of 

Doctor of Philosophy 

DEVELOPMENT AND VALIDATION OF A GROWTH MODEL FOR 
FLORIDA-GROWN SOYBEANS WITH DISEASE STRESS 

By 

Steven Boyd Johnson 

December, 1982 

Chairman: Dr. Richard D. Berger 
Major Department: Plant Pathology 

Holistic field experiments with soybean were 
conducted over two growing seasons, in which fungicides 
were applied to achieve various disease intensities. 
The experiments were monitored at weekly intervals from 
planting through harvest. Each week, the disease 
intensity of five different foliar diseases, the insect 
damage, and the area of each individual leaf were 
measured from representative plants. The above-ground 
portion of the plants, which had been measured through 
the growing season, were harvested for dry-matter 
yields. The experiments continued after harvest when 
the pathogen presence in the seed from the individually 
monitored plants and in the seed from the plots was 
det ermined . 



X 



A narrow range of disease intensities resulted from 
the application of the fungicides and, consequently, 
yield differences were nonsignificant. However, quality 
of the soybean pods and stems from the individually 
monitored plants were significantly improved with the 
application of a fungicide. 

A regression-derived soybean growth model was 
developed from the holistic field experiments. The 
model was driven by the dry-matter accumulation of 
soybean leaves over time. Separate equations which 

described the dry-matter accumulation of soybean stems, 
pods, petioles, and seeds were developed and related to 
the leaf dry-matter accumulation equation by ratios, 
updated with each time step. 

Model validation was performed with measurements 
taken from holistic field experiments conducted over two 
growing seasons. The measurements were independent of 
the measurements used to develop the model. Disease 
intensities were too low over both growing seasons for a 
true yield difference to be realized from the fungicide 
appl icat ions . 

Model verification was performed with published 
values. Dry-matter ratios and slopes of dry-matter 

accumulation for soybean organs, as described by the 
model, were comparable with published values. 



XI 



Theoretical submodels were developed for foliar 
pathogens, above-ground non-foliar pathogens, 

foliage-feeding insects, and seed-feeding insects. 
Possible applications for the model are to develop 
disease intensity - yield loss relationships and 
threshold levels of disease-induced loss. 



xii 



I. GENERAL INTRODUCTION 



The soybean ( Glycine max (L.) Merrill), an annual 
plant, is included in the kingdom Planta, phylum 

Tr acheophy ta , class Angiospermae , order Rosales, family 
Leguminosae, and is one of the oldest cultivated crops. 
The geographic adaptability, high oil content, and high 
nutritive value of the pressed seed mash have placed the 
soybean into prominence in United States agriculture. 
Since 1954, the United States has been one of the 
world's largest producer of soybeans. 

Throughout the geographical distribution of the 
soybean, diseases reduce yield quality and quantity. 
The most prevalent fungal diseases of soybean in Florida 
are purple stain (Cercospora k iku ch i i Matsumoto & 
Tomoyasu), frogeye leaf spot (Cercospora so j ina Hara.), 
downy mildew (Peronospora manshur ica (Naoum.) Syd. ex 
Gaum.), target spot ( Corynespora cassiicola (Berk, and 
Curt.) Wei.), Rhizoctonia blight (Rhizoctonia solani 
Kuehn), anthracnose ( Collet o t r i chum truncatum (Schw.) 
Andrus and W. D. Moore), and pod and stem blight 
( Phomopsis so j ae Leh. {Diaporthe soiae Leh.}) {10, 18, 
75}. Diseases which cause yield reduction and plant 
defoliation in other soybean growing regions are not 



1 



2 



present (rust { Phakopsora pachvrhiz i Sydow}) or not 
prevalent in some portions of Florida (brown spot 
{Septoria glycines Hemmi.}). The devastation of soybean 
in areas of the world by diseases, some presently in 
Florida, necessitates the establishment of disease 
intensity-yield loss relationships. 

The relationship between foliar diseases and yield 
of soybeans has not been characterized. One approach to 
determine the disease intensity-yield loss relationship 
would be to measure the host growth without stress 
(diseases, insects, drought, poor nutrition) and 

concurrently subject other host plants to various levels 
of stress and measure the resulting change in yield. 

Both the host growth and stress components can be 
described mathematically. The host growth model, with 
and without the stress factors, would need validation 
and verification to prove its applicability for decision 
processes in soybean crop management. In the case of 
this project, the controlled stress factors were 
diseases. In the growth model, leaf area could be 
mathematically removed to simulate stress. The 
influence of disease on yield can be examined by 
removing leaf area from f ung i c ide- treat ed plants 

equivalent to the disease levels on non-treated plants. 

The approach of modeling will help field projects 
become more efficient, or at least can help direct 



3 



investigations toward aspects of the pathosystem which 
need clarification. 

Any model is the simplified representation of a 
system. The soybean growth model developed with disease 
stress, described here, does not cover all aspects of 
plant growth, but that was not its intended purpose. 
The purpose of this new model was to be a beginning 
framework for a disease intensity - yield loss model. 
Further model refinements could incorporate pathogen 
response to environmental conditions and growth of 
systemic, non-foliar pathogens. 

Data were analyzed on an Amdahl 470 V6 and an IBM 
3033N at the facilities of the Northeast Regional Data 
Center of the State University System of Florida, and on 
an APPLE II Plus microcomputer (48 K memory); all 
computers were located on the University of Florida 
campus, Gainesville, Florida. 



II. LITERATURE SURVEY 



Morse (62), in reviewing the ancient history of the 
soybean, claimed that the first written record of the 
soybean was 2838 B. C. It was not until around 1900 
that commercial soybean acreage became established in 
the United States. At that time, the soybean acreage 
was grown mainly for forage. As late as 1934, only 25% 
of the soybean acreage in the United States was 
harvested for seed. By 1941, more soybean acreage was 
harvested for seed than for forage. Today, the soybean 
acreage grown for forage in the United States is 
minimal . 

A model is a simplifed representation of a system. 
A system is a collection of components and their 
interrelationships. A model, therefore, represents a 
collection of components, or more often a component and 
its inputs and outputs. A model which covers all 
aspects of crop growth defeats its purpose. Such a 
model would be too large to solve detailed problems 
which arise under field conditions. The simpler the 
model is, while retaining accuracy, the more effective 
the model becomes. Simple models are easier to evaluate 
critically, and easier to apply to wide ranges of 



4 



conditions. A model can be used to predict the system 
response to various changes in inputs, without going to 
the actual system. The usefulness of models for 
research programs, whether for identification of 
problems or solving problems, cannot be overstated. 

Modeling of systems dates back as far as records 
were kept, and yet is as current as today. Development 
of maps, models of land or water masses, continuing 
through the development of laws of physics, modeling the 
effect of gravity, etc., to today's high-speed 
digital-computer simulations of biochemical processes, 
are examples of models. Likewise, soybeans are one of 
the world's oldest cultivated crops, yet today are under 
intensive investigation to increase yields. Soybeans, 
like modeling, can be viewed as rich in tradition and 
yet extremely modern. 

Soybeans as a crop and modeling as a science 
started to converge with the interest of agricultural 
scientists in yield maximization. Early modelers of 
soybean growth were concerned with production of 
soybeans as a forage source, and the effect of 
defoliation (cattle grazing) on leaf, stem, and seed 
yield (26). The maximization of the entire soybean 

plant was investigated in this work, because the soybean 
was grown as a forage source. Interest shifted from 
modeling the injury from grazing to modeling the injury 



6 



from hail (25, 85). More recently, insect damage (9, 
34, 80, 81) and disease damage (43) have been modeled. 
McAlister and Krober (46) approached the situation 
differently; they measured the yield changes and the 
changes in the biochemical components of the soybean 
plant parts in response to artificial defoliation. The 
recent soybean modeling work has focused on seed yield 
maximization, as soybean production is now almost 
exclusively a seed crop. Soybean workers have 

investigated several diseases and their relation to 
yield loss (17, 36, 40, 41, 86). At present, the models 
are, at best, restricted to a particular location for a 
particular season. A flexible model for soybean growth 
which would give loss estimates from diseases does not 
exist. None of the models for soybean disease loss have 
been incorporated into growth models. 

Paralleling the interest in defoliation - yield 
loss relationships for soybeans were those of other 
crops. Chester (13) published an article on the nature 
of artificial defoliation experiments to provide a basis 
for disease-loss estimates. Chester drew from studies 
involving wheat, corn, oats, barley, and onion. 

Dis eas e- lo s s , hail-loss, or insect-loss estimates are 
the goal of the models which focused on seed-yield 
maximization. While the relationship for defoliation 

level - yield reduction in soybean were under 



7 



development, new research needs surfaced. Quantitative 
descriptions of dry-matter accumulation for yield 
components were required for soybean models dealing with 
plant growth. From the 1960's and continuing through 

today, mathematical models of dry-matter accumulation 
for soybean have been published. Shibles and Weber (72, 
73) were pioneers in these studies, establishing that 
soybean plant dry-matter accumulation was proportional 
to leaf area. The early work by Shibles and Weber has 
been further documented by numerous workers (8, 20, 21, 
22, 28, 29, 31, 71). The linear relationship between 
the dry-matter accumulation for entire soybean plants 
versus time has been documented (28, 31, 71). The 
linear relationship for the dry-matter accumulation for 
the seed versus time (8, 20, 21, 28) has also been 
described. The development of quantitative descriptions 
of dry-matter accumulation for soybean yield components 
provided the data for soybean modeling. 

In modeling efforts, the soybean is a relatively 
new crop, when compared to potatoes or cereals. To 
date, soybean models, especially soybean disease models, 
are lacking the sophistication and accuracy of those of 
potato and cereal crops. Much progress on modeling of 
the soybean has been made in the last twenty years, and 
with the ever- in cr e a s ing importance of the soybean as a 
crop, progress toward maximization of soybean yields 
will continue to be made. 



III. DEVELOPMENT OF THE MODEL 



A.) Introduction 

Holistic experiments were conducted in 1980, and 
repeated during the 1981 growing season. The 
experiments were designed to follow the host growth 
during the season and to observe the development and 
progress of all foliar-disease epidemics. On selected 
plants, all leaves were regularly examined for disease 
incidence and severity and for leaf area. All 
measurements were made by nondestructive methods. 
Fungicides were applied frequently, or not at all, to 
provide a range of diseases and disease intensities. 
Frequent fungicide applications were aimed at providing 
as complete disease control as possible. Some plots 
were left untreated by fungicides to allow disease 
epidemics to progress without man-made interference. 
For both growing seasons, the plots were located within 
a 2 hectare soybean field, with approximately 4 to 6 
additional hectares of soybeans grown in close 
proximity. Soybeans had been grown continuously in the 

general area for over 10 years, assuring some presence 
of inoculum for diseases commonly occurring in Florida. 



8 



9 



The growth model was derived from host 
measurements. Disease intensity measurements over time 
provide an account of disease progress, which could be 
converted into a disease model. The separation of host 
and disease is a useful approach for the determination 
of economic threshold levels for diseases. Mathematical 
separation of host and disease could be accomplished by 
mathematically increasing the disease intensity in the 
disease model and removing the increased amounts of 
diseased tissue from the total host tissue. 



B.) Materials and Methods 



The soybean breeding line F76-4486 was chosen for 
the experiments. The breeding line is susceptible to 
some foliar diseases and is an F5 line from the cross 
Centennial x (Forrest x {Cobb x D68-216}) (K. Hinson, 
personal communication ) ; the cultivar Foster (F76-8827) 
is a sibling to the chosen breeding line. The 
experimental fields were located on the University of 
Florida campus, Gainesville, Florida. The plots were 
arranged in a r andomiz ed-c omp 1 e t e-b lo ck design, with 
five replications of fungicide treatments. The 
fungicide treatments were benomyl, benomyl plus 

metalaxyl, metalaxyl, and an untreated check (Appendix 
A). The plots were four 6.1-meter long rows with 



10 



0.91-meter row spacing and were seeded at the rate of 39 
s eed s /me t er . The center two rows were used as multiple 
observations from each experimental unit. This allowed 
for testing of block by treatment interaction. The 
field was fertilized and an insecticide applied as 
recommended for the crop (2). Benomyl was applied 
(586.5 g/ha) at 14-day intervals; metalaxyl was applied 
(1137 g/ha) at 40-day intervals. All fungicide 
applications were made over the top of the canopy with a 
hand-held, two-row, six-nozzle, boom sprayer delivering 
the equivalent of 396 Is/ha at 276 kilopascals (Appendix 
B). Frequent fungicide applications were used to 

provide optimal disease control. Metalaxyl and benomyl 
were chosen because their selectivity of action would 
provide a difference in disease severity and intensity 
in the plots. Metalaxyl was chosen to control downy 

mildew. Benomyl was chosen to control other diseases, 
primarily pod and stem blight, anthracnose, frogeye leaf 
spot, and purple stain. 

In each of the center two rows of a plot, plants 
were randomly selected and labeled. Starting three 

weeks from date of sowing, weekly measurements of leaf 
area and disease incidence and severity were recorded 
for each leaf of the labeled plants. Leaf emergence, 
leaf senescence, insect damage, and growth stage (23, 
24) also were recorded weekly. For each leaf, the 



11 



disease and insect damage was expressed as a percent of 
leaf area for that leaf. All measurements were 
comparisons against leaf-area diagrams and insect- and 
disease-rating scales developed for soybeans (Appendix 
C). Total photosynthetic area of the leaves was 

calculated by subtracting the senescent, insect-damaged, 
and diseased area from total leaf area. 

At maturity, each of the labeled plants was 
harvested. The dry weights for pods, seeds, and stems 
were measured from each plant. The disease severity on 
the stems and pods from these plants was rated on a 
scale from to 10. The rating scale consisted of 11 
evenly spaced ratings with a rating of for 0% of the 
pods or main stem covered with symptoms or signs of 
pathogens; and a rating of 10 for 100% of the pods or 
main stem covered with symptoms or signs of pathogens. 
Because the visual ratings had a binomial distribution, 
an angular transformation (arcsine (disease 

proport ion) ** { 1 / 2 } ) was used where the ratings of 0, 1, 
2, 3, 4, 5, 6, 7, 8, 9, 10 were equivelent to 0, 2.5 10, 
21, 35, 50, 65, 79, 90, 97.5, 100%, respectively (42). 
Seeds from the labeled, individually harvested plants 
were surface sterilized in a 10% Clorox (Appendix D) 
solution for one minute and plated onto acidified potato 
dextrose agar (Appendix D) and observed within seven 
days for presence of seed-borne fungal pathogens. After 



12 



the labeled plants were individually harvested, the 
center 4.88 meters of the center two rows were harvested 
for seed yield. 

1 . ) Field Plots . 1980 

The field was planted on 27 June and harvested 125 
days later on 4 November. Benomyl was first applied 4 
July, and sunsequent applications were made every 14 
days through harvest. Metalaxyl was first applied 30 
June, and sunsequent applications were made every 40 
days through harvest. Leaf area and disease intensity 
data were collected from five plants in each of the 
center two plot rows. 

2.) Field Plots, 1981 

The field was planted on 15 June and harvested 124 
days later on 16 October. Benomyl was first applied 22 
June, and sunsequent applications were made every 14 
days through harvest. Metalaxyl was first applied 22 
June, and sunsequent applications were made every 40 
days through harvest. Leaf area and disease intensity 
data were collected from two plants in each of the 
center two plot rows. 



13 



In a separate experiment, the soybean breeding line 
F76-884b was planted on the University of Florida 
campus, Gainesville, Florida. The separate experiment 
was conducted to ascertain if the scheme of benomyl 
application affected the disease severity or yield. The 
treatments were arranged in a r andomized-complete-block 
design with five replications of benomyl (586.5 g/ha) 
applied i) at planting, ii) at 11 and 13 weeks from 
planting, iii) at planting and at 11 and 13 weeks from 
planting, iv) or not at all. The plots, fungicide 
applications, and harvested plant matter were treated as 
described above. 

C.) Results 

1 .) Field Plots . 1980 

The block by treatment interaction was 
nonsignificant (P=0.23) for all measured variables; so 
the plots were treated as an entity, not as two 
individual rows. The seed yields from the plots were 
not significantly different among the treatments (Table 
1). Dry weight yields from the labeled, individually 

harvested plants were not significantly different among 
treatments (Table 2) when viewed as entities (P=0.38) or 
when separated into seeds (P=0.39), stems (P=0.35), and 



14 



Table 1. Soybean seed yields from plots, 1980. 



Treatment 

Benomyl + 
metalaxyl 

Benomy 1 

Metalaxyl 

Check 

Duncan-Wal ler 
k-ratio (k=100) 
LSD 



Seed yield 

g/plot kg/ha 

2676^ 3200 

2564 3067 

2128 2545 

2393 2862 

NS 



Benomyl was applied at the rate of 586.5 g/ha at 14-day 
intervals from 27 June 1980 to 4 November 1980. 
Metalaxyl was applied at the rate of 1137 g/ha at 40-day 
intervals from 27 June 1980 to 4 November 1980. Check 
plots received no treatment. 

Mean of five plots. 



15 



Table 2. Dry-matter yields from individually harvested 
soybean plants, 1980. 



Treatment'^ Yield/plant (g)^ 

Total Partitioned 

Pods Stems Seeds 



Benomyl + 



metalaxyl 


22 


.10 


4 


.21 


6 


.98 


10 


.91 


Benomy 1 


19 


.20 


3 


.59 


5 


.86 


9 


.75 


Metalaxyl 


20 


.54 


3 


.96 


6 


.92 


9 


.66 


Check 


16 


.40 


3 


.08 


5 


.57 


7 


.75 



Duncan-Waller 
k-ratio (k=100) 

LSD NS NS NS NS 



^Benomyl was applied at the rate of 586.5 g/ha at 14-day 
intervals from 27 June 1980 to 4 November 1980. 
Metalaxyl was applied at the rate of 1137 g/ha at 40-day 
intervals from 27 June 1980 to 4 November 1980. Check 
plots received no treatment. 

z 

Mean of 50 plants. 



16 



pods (P=0.42). The quality of the stems and pods from 
the labeled, individually harvested plants, as 

determined by visual rating, varied significantly in 
response to the treatments. Treatments of benomyl and 
benomyl plus metalaxyl had significantly better disease 
control on pods (P=0.0001) and stems (P=0.0001) than did 
treatments without benomyl (Table 3). Individually 
harvested plants from the benomyl treatment had the 
lowest percent seed infection (5%) and the untreated 
check had the highest percent seed infection (15%) 
{Table 4}. The ratios of the percent seed infection 
caused by Fusar ium spp., Phomop s i s spp., and C^. kikuchii 
were similar across the treatments. This was also true 
when the seed from the individually harvested plants 
were plated (Table 5). 



2.) Field Plots, 1981 



The block by treatment interaction was 
nonsignificant (P=0.25) for the seed yield from the 
plots, so plots were treated as an entity, not as two 
individual rows (Table 6). A significant block by 
treatment interaction was present for the dry-weight 
yields of the labeled, individually harvested plants 
when separated into seeds (P=0.04), pods (P=0.04), or 
when the dry weights of the seeds, pods, and stems were 



17 



Table 3. Pod and stem quality ratings from individually 
harvested soybean plants, 1980. 



Treatment 



Pod 



Rating^ 



Stem 



Benomyl + 
metalaxy 1 

Benomy 1 

Metalaxy 1 

Check 



22 

2 
7 
7 



4 
4 
8 
8 



Duncan-Waller 
k-ratio (k=100) 
LSD 



Benomyl was applied at the rate of 586.5 
intervals from 27 June 1980 to 4 November 
Metalaxyl was applied at the rate of 1137 
intervals from 27 June 1980 to 4 November 
plots received no treatment. 



g/ha 
1980 
g/ha 
1980 



at 14-day 

at 40-day 
Check 



^Rated on a scale of to 10 and transformed with an 
angular transformation (arcsine (disease 

pr opor t ion ) ** { 1 / 2 } ) where the ratings of 0, 1, 2, 3, 4, 
5, 6, 7, 8, 9, 10 were equivelent to 0, 2.5 10, 21, 35, 
50, 65, 79, 90, 97.5, 100%, respectively (42). 



Mean of 50 plant s . 



18 



Table 4. Soybean 


seed 


infection from 


plots , 


1980 


V 

Treatment 


^ Percent 


w 

seed infection 






T" 




P 


C 





Benomyl plus 
metalyxl 


07^ 


82 


08 


02 


08 


Benomy 1 


21 


60 


07 


02 


31 


Metalaxy 1 


05 


48 


20 


28 


04 


Check 


05 


78 


06 


06 


11 



^Benomyl was applied at the rate of 586.5 g/ha at 14-day 
intervals from 27 June 1980 to 4 November 1980. 
Metalaxyl was applied at the rate of 1137 g/ha at 40-day 
intervals from 27 June 1980 to 4 November 1980. Check 
plots received no treatment. 

^Seven hundred seed plated onto acidified potato 
dextrose agar and observed 3 to 5 days later. 

T=total seed infection. 

^Total seed infection partitioned into specific 
pathogens: F= Fusar ium spp.; P = Phomop s is spp.; 
C = Cer CO s por a kikuchii ; = Others, primarily Rhizoctonia 
spp. and Colletotri chum spp. 

Round off error present. 



19 



Table 5. Soybean seed infection from individually 
harvested plants, 1980. 



w 

Treatment 

Benomyl plus 
metalyxl 

Benomy 1 

Metalaxy 1 

Check 



T^ 


Percent 
F 


seed 
P 


infection 
C 





10 


67 


12 


12 


09 


05 


66 


17 


17 


00 


08 


60 


20 


20 


00 


15 


69 


20 


11 


00 



^Benomyl was applied at the rate of 586.5 
intervals from 27 June 1980 to 4 November 
Metalaxyl was applied at the rate of 1137 
intervals from 27 June 1980 to 4 November 
plots received no treatment. 



g/ha at 14-day 
1980 . 

g/ha at 40-day 
1980. Check 



Four hundred seed plated onto acidified potato dextrose 
agar and observed 3 to 5 days later. 

y 

T=total seed infection. 

Total seed infection partitioned into specific 
pathogens: F= Fusar ium spp.; P = Phomop s i s spp.; 
C = Cercospora kikuchi i ; = Others, primarily Rhizoctonia 
spp. and Co 1 letotr ichum spp. 



20 



Table 6. Soybean seed yields from plots, 1981. 



Treatment 

Benomyl + 
metalaxy 1 

Benomy 1 

Metalaxy 1 

Check 

Duncan-Wal ler 
k-ratio (k=100) 
LSD 



Seed yield 
g/plot kg/ha 



1071^ 


1281 


898 


1074 


771 


922 


1002 


1198 


197 





Benomyl was applied at the rate of 586.5 g/ha at 14-day 
intervals from 22 June 1981 to 16 October 1981. 
Metalaxyl was applied at the rate of 1137 g/ha at 40-day 
intervals from 22 June 1981 to 16 October 1981. Check 
plots received no treatment. 

y 

Mean of five plots. 

z 

Round-off error present. 



21 



summed (P=0.07); a significant block by treatment 
interaction was absent for the dry weight of stems 
(P=0.36). Much of the block by treatment interaction 
was attributable to block 3 (Table 7). With block 3 
removed from the calculations (taking a mean of the 
remaining four blocks), block by treatment interactions 
were nonsignificant for dry-weight yields of the 
labeled, individually harvested plants when separated 
into seeds (P=0.19), pods (P=0.16), stems (P=0.59), or 
when the dry weights of the seeds, pods, and stems were 
summed (P=0.26). With blocks 1, 2, 4, and 5 summed, 
there were significant differences in the labeled, 
individually harvested plants when separated into stems 
(P=0.02), pods (P=0.02), and seeds (P=0.03), or when 
summed (P=0.02); the nonsignificant block by treatment 
interaction was used as an error term for these tests 
(Table 8). The visible quality of the stems and pods 
from the labeled, individually harvested plants varied 
significantly in response to treatments. Block by 
treatment interaction was nonsignificant for visual 
ratings of quality for pods (P=0.27) and stems (P=0.81). 
Treatments with benomyl (benomyl alone, and benomyl plus 
metalaxyl) had significantly less (P=0.0001) symptoms 
and signs of pathogens than did treatments without 
benomyl (metalaxyl alone, and check), when the 

nonsignificant block by treatment interaction was used 



22 



Table 7. Dry-matter yields from individually harvested 
soybean plants, 1981. 



Portion Yield/plant (g)^ 

Block 



Pods M-^14.65a M 11.88a B 4.37 M 13.55a M 7.93 

6.98 
4.99 
4.75 
NS 





1 






2 




3 






4 




M^14 


.6 5a^ 


M 


11 


.88a 


B 


4.37 


M 


13 


.55a 


M 


9 


.82 b 





9 


.25ab 


C 


3.92 


B 


5 


.16 b 


B 


C 3 


.61 c 


B 


6 


.30 b 


M 


3.91 


C 


4 


.77 b 


C 


B 3 


.53 c 


C 


5 


.76 b 





3 .32 





4 


.28 b 

















NS 











Seeds M 32.43a M 26.14a B 9.86 M 29.74a M 17.45 

22.79 b 21.42ab 8.27 B 12.70 b B 16.12 

B 8.83 c B 15.85 be C 7.89 C 10.39 b C 11.94 

C 8.53 c C 13.20 c M 6.40 9.58 b 11.55 

NS NS 



M 


15 


.63a 


M 


13 


.32a 


B 


5.50 


M 


16 


.02a 


M 





11 


.39 b 





12 


.45a 


C 


4.91 


C 


7 


.49 b 


B 


B 


5 


.98 c 


B 


8 


.37 b 


M 


4.70 





6 


.24 b 


C 


C 


5 


.57 c 


C 


8 


.14 b 





4.52 
NS 


B 


5 


.83 b 






9.69 
8.23 
7 .91 
NS 



Total M 62.71a M 51 .34a 

44.00 b 43 .12ab 

B 18.34 c B 30.52 b 

C 17.70 cC 27.10 c 



Roundoff error present. 
Mean of four plants. 

y 

B=benomy 1-treated plants; M=metalaxy 1-treated plants; 
O=benomyl plus me t a 1 axy 1- t r e a t ed plants; C=check plants. 
Benomyl was applied at the rate of 586.5 g/ha at 14-day 
intervals from 22 June 1981 to 16 October 1981. 
Metalaxyl was applied at the rate of 1137 g/ha at 40-day 
intervals from 22 June 1981 to 16 October 1981. Check 
plots received no treatment. 

z . . . 

Numbers in the same column, within a row, followed by 

the same letter are not significantly different 
according to Duncan's new multiple range test (P=0.05). 



B 19.73 
C 16.72 
16.11 
M 15.00 
NS 



M 59.31a 
B 23.68 b 
C 22.65 b 
20.09 b 



M 36.52 
B 32.79 
C 25.16 
24.20 
NS 



23 



Table 8. Dry-matter yields from individually harvested 
soybean plants (analyzed with block three removed), 
1981 . 



Treatment 



w 



Yield/plot (g) 
Total Partitioned 



Pods 



S t ems 



Seeds 



Benomyl + 
metalaxy 1 

Benomy 1 

Metalaxy 1 

Check 



32 


•BSa^^ 7 


.03a 


9 


.50a 


16 


.34a 


26 


.33a 5 


.49a 


7 


.47a 


13 


.38a 


52 


.47 b 12 


.03 b 


14 


.03 b 


26 


.44 b 


23 


.15a 4 


.78a 


7 


.36a 


11 


.02a 



^Benomyl was applied at the rate of 586.5 
intervals from 22 June 1981 to 16 October 
Metalaxyl was applied at the rate of 1137 
intervals from 22 June 1981 to 16 October 
plots received no treatment. 

Mean of 16 plants. 

^Numbers within a column followed by the same letter are 
not significantly different according to Duncan's New 
Multiple Range Test (P=0.05). 

Roundoff error present. 



g/ha at 14-day 
1981 . 

g/ha at 40-day 
1981. Check 



24 



as an error term (Table 9). Seed infection from the 
individually harvested plants was low. The most likely 
reason for the low infection levels would be the weather 
conditions (57-61) which were highly unfavorable for 
pathogen development. The same soybean line was used in 
1980 and 1981 and the plots were located about 100 
meters from where the 1980 plots had been planted; so 
inoculum present during 1981 should have been similar to 
that of 1980. A range of 6% in seed infection levels 
was present (Table 10). Viewed differently, 

benomy 1- t r ea t ed versus non-benomy 1-treated , the 

percentages of seed infection were 3% versus 4%. True 
differences would be expressed in this comparison. 
Another test to determine if random infection existed 
would be to compare the percent of seed infection from 
metalaxy l-treated (benomyl plus metalaxyl, and metalaxyl 
alone) versus non-metalaxy 1-treated (benomyl alone, and 
check) plants, 3% versus 3% in this case. The percent 
of infected seed from the individually harvested plants 
(Table 11) was similar to that from the plots. The low 
level of infection present in 1981 precluded meaningful 
ana ly s is. 

In the separate experiment on the time of 
application of benomyl, the block by treatment 

interaction was significant (P=0.03) for the seed yield; 
so the seed yield was analyzed separately for each block 



25 



Table 9. Pod and stem quality ratings from individually 
harvested soybean plants, 1981. 



Treatment^ Rating^ 





Pod 


Stem 


Benomyl + 
metalaxyl 


la^ 


la 


Benomy 1 


la 


la 


Metalaxyl 


2 b 


7 b 


Check 


2 b 


7 b 



^Benomyl was applied at the rate of 586.5 g/ha at 14-day 

intervals from 22 June 1981 to 16 October 1981. 

Metalaxyl was applied at the rate of 1137 g/ha at 40-day 

intervals from 22 June 1981 to 16 October 1981. Check 
plots received no treatment. 

Mean of 20 plants. 

^Rated on a scale of to 10 and transformed with an 
angular transformation (arcsine (disease 

proportion)**{l/2} ) where the ratings of 0, 1, 2, 3, 4, 
5, 6, 7, 8, 9, 10 were equivelent to 0, 2.5 10, 21, 35, 
50, 65, 79, 90, 97.5, 100%, respectively (42). 

Numbers followed by the same letter are not 
significantly different according to Duncan's New 
Multiple Range Test (P=0.05). 



26 



Table 10. Soybean seed infection from plots, 1981. 



^ w 
Treatment 


T^ 


Percent 
F 


seed 
P 


infection^ 
C 





Benomyl plus 
metalyxl 


02 


64 


27 


09 


00 


Benomy 1 


03 


31 


62 


07 


00 


Metalaxy 1 


04 


32 


26 


37 


05 


Check 


02 


00 


88 


12 


00 



w 

Benomyl was applied at the rate of 586.5 
intervals from 22 June 1981 to 16 October 
Metalaxyl was applied at the rate of 1137 
intervals from 22 June 1981 to 16 October 
plots received no treatment. 

X 

Five hundred seed plated onto acidified potato dextrose 
agar and observed 3 to 5 days later. 

y 

T=total seed infection. 

Total seed infection partitioned into specific 
pathogens: F= Fusar ium spp.; P = Phomops is spp.; 
C =Cercospora kikuchii ; 0=Others, primarily Rhizoctonia 
spp. 



g/ha at 14-day 
1981 . 

g/ha at 40-day 
1981. Check 



27 



Table 11. Soybean seed infection from individually 
harvested plants, 1981. 



w 

Tr eatment 




Percent seed infection 






1 


f 


P 


C 


Benomyl plus 












metal yx 1 




02 


50 


00 


50 


Benomy 1 




03 


50 


50 


00 


Metalaxy 1 




01 


100 


00 


00 


Check 




07 


00 


67 


33 


^Benomyl was a 


ppl 


ied at 


the 


rate of 586.5 


intervals from 


22 


June 


1981 


to 16 Oct 


ober 


Metalaxyl was 


applied a 


t the 


rate of 


1137 


intervals from 


22 


June 


1981 


to 16 Oct 


ober 



981. Check 
plots received no treatment. 

One hundred seed plated onto acidified potato dextrose 
agar and observed 3 to 5 days later. 

y 

T=total seed infection. 

z 

Total seed infection partitioned into specific 
pathogens: F = Fusar ium s p p . ; P = Phomopsis s p p . ; 
C = Cerco spora kikuchii. 



28 



(Table 12). The block by treatment interaction was 
nonsignificant for the visual ratings of the pods 
(P=0.62) and stems (P=0.70); so the plots were treated 
as an entity, not as two individual rows. Plots treated 
with benomyl at weeks 11 and 13 yielded significantly 
higher quality pods (P=0.0004) and stems (P=0.0001) than 
those plots treated at planting only or not treated at 
all; data are presented in Table 13. Seed from the 
harvested rows were plated as described, and results 
appear in Table 14. The low level of seed infection 
prevented meaningful analysis. 

D.) Discussion 



1 . ) Field Plots . 1980 

The foliar diseases observed were purple stain, 
frogeye leaf spot, downy mildew, target spot, and 
Rhizoctonia blight. The actual amount of foliar disease 
on individual plants ranged from less than 0.01% to 
10.0%. The 10% disease severity was approached 

immediately prior to final leaf drop. 

Throughout the growing season, few individual 

leaves reached a 10% disease level; generally, 
senescence would occur and the leaf would be removed 
from the canopy. It was not possible to partition the 



29 



Table 12. Soybean seed yields from separate experiment, 
1981 . 



Benomy 1 
application^ 



Yield/plot (g) 
Block 
2 3 



Plant ing 



801 b 1243a 



Plant ing , 
weeks 11&13 1154a 



Weeks 11&13 
Check 



798 b 
1203a 



999 b 
836 b 
778 b 



815 

877 
956 
727 
NS 



1113a 



1154 



807 b 1224 
1002ab 1083 
1051ab 1242 
NS 



Benomyl was applied at the rate of 586.5 g/ha; the 
field was planted on 15 June 1981. Check plots received 
no treatment . 

z 

Numbers within the same column followed by the same 
letter are not significantly different according to 
Duncan's new multiple range test (P=0.05). 



30 



Table 13. Soybean pod and stem quality ratings from 
separate experiment, 1981. 



Benomyl ^ x 
application Rating 

Pod Stem 



Planting 2 a^^ 5 b 
Planting , 

weeks 11&13 lb 2c 

Weeks 11&13 lb 2c 

Check 3a 7a 



Benomyl was applied at the rate of 586.5 g/ha; the 
field was planted on 15 June 1981. Check plots received 
no treatment . 

X 

Mean of 10 ratings. 

y 

Numbers within the same column followed by the same 
letter are not significantly different according to 
Duncan's new multiple range test (P=0.05). 

z 

Roundoff error present. 



31 



Table 14. Soybean seed infection from separate 
experiment, 1981. 



Benomyl ^ 
application 




Percent seed 


inf ect ion 
P 


C 


Planting 


03 


54 


23 


23 


Planting, weeks 
11 and 13 


05 


55 


35 


10 


Weeks 11 and 13 


03 


71 


29 


00 


Check 


03 


50 


29 


21 



^Benomyl was applied at the rate of 586.5 g/ha; the 
field was planted on 15 June 1981. Check plots received 
no treatment . 

^Five hundred seed plated onto acidified potato dextrose 
agar and observed 3 to 5 days later. 

y , 

T=total seed infection. 

^Total seed infection partitioned into specific 
pathogens: F= Fusar ium spp.; P = Phomop s i s spp.; 
C = Cerco spora kikuchii . 



32 



senescent leaf area into d i s e as e- indu ce d senescence and 
normal senescence. The low levels of foliar disease 
present throughout the growing season could have been a 
direct result of the dry environmental conditions 
(51-56) and may have been the reason why seed yields 
from the plots were not significantly different when the 
Duncan-Waller k-ratio (k=100) LSD test was used. All 
yields were high — about 3000 kg per hectare. 
Plant-portion dry weights, partitioned into pods, stems, 
and seeds, or combined as whole plants, were not 
significantly different among treatments. The 
nonsignificant differences in the dry weights were 
expected when seed yields from the plots were not 
significantly different. High variability present in 
plants grown under field conditions may partially 
account for the nonsignificant differences among weights 
of plants from the treatments. The weight differences 
were not large enough to be significantly different at 
P=0.05, probably because of low disease levels. 

The weather conditions for the 1980 growing season 
(51-56) were not excessively conducive to spread of 
foliar diseases. Foliar diseases did not cause 

appreciable plant stress, and neither did water 
deficiency during the growing season. Overhead 

irrigation was applied three times during the growing 
season. The timing of the irrigation applications 



33 



undoubtedly was not optimal, as soil tensiometers were 
not used. No irrigation management scheme was followed, 
so it would be improper to conclude that irrigation was 
applied to produce optimal yields or favor disease 
spread. The foliar disease levels ranged from less than 
0.01% to 1.0% throughout most of the growing season and 
the nonsignificant differences in seed yield of plots as 
a result of fungicide treatments were expected. 

The lack of a yield response as a result of disease 
control manifested itself in soybean fungicide trials 
conducted by others during the season (5, 19, 65, 66, 
67, 83). In some reports, foliar disease levels were 
not significantly different (P=0.05) for frogeye leaf 
spot (65, 67), purple stain (66), or for pod 
discoloration (66). Pod discoloration (67), purple 
stain (67), and frogeye leaf spot (66) levels were 
significantly different (P=0.05) for other tests. No 
significant (P=0.05) yield response as a result of 
fungicide treatment was present across most fungicide 
trials during that year. Plots with treatments which 
controlled diseases generally yielded better than those 
plots which did not have a fungicide treatment, although 
not always significantly (P=0.05). The general trend 
observed was the highest control of foliar disease 
provided the highest yield of plant mass. I also 

observed the same trend of highest yield associated with 



34 



greater control of disease. 

The seed yields were not significantly different 
among the treatments. Treatments which received benomyl 
produced stems and pods with significantly fewer signs 
and symptoms of pathogens than did treatments without 
benomyl. Low levels of disease were present during the 
growing season but highly significant differences in the 
presence of symptoms and signs of pathogens on the stem 
and seed were present. The low disease levels during 
the growing season resulted in low seed-infection 
levels. Had more disease been present, the range of the 
percent infected seed may have been wider than 10% 
between seed from untreated-check plants and seed from 
fungicide-protected plants. Seed from the individually 
harvested plants of different treatments was infected by 
fungi to varing degrees. Fungicide application reduced 
the percentage of seed infection, but did not reduce the 
relative importance of the pathogenic fungi. Fusar ium 
spp. was responsible for about 65% of the seed 
infection, Phompo sis spp. for about 17%, and ^ kikuchii 
for about 15%, irrespective of the treatment. Plants 
which received a benomyl treatment, with or without 
metalaxyl, had 8% seed infection, compared to 12% for 
seeds from plants which had not received a benomyl 
treatment. The majority of pathogens cultured from the 

sur f ac e- s t er i 1 iz ed seeds were inhibited by the presence 



35 



of benomyl, but the low numbers of infected seeds 
precluded meaningful analyses; benomyl treatments had 4% 
lower seed infection than non-benomyl treatments, but 
this was considered inconsequential. Seed from plants 
which received a metalaxyl treatment (with or without 
benomyl) had 9% infection compared to 10% for seeds from 
plants which had not received a metalaxyl treatment. 
These values should be similar, because in each case, 
half of the plots received benomyl and half did not. 
Metalaxyl, specific-acting toward Pythiacious fungi, had 
no effect on Fusar ium spp., Phomopsis spp., or C_. 

kikuchii . Metalaxy 1-treated plants would not be 

expected to have reduced seed infection when compared to 
the untreated plants because P. manshurica will not grow 
on acidified potato dextrose agar. Seed pathogens 

occurred at similar ratios, irrespective of the 
treatment. Pathogen escapes would be expected to occur 
at similar frequencies to pathogens present in seeds 
from the untreated-check plants. 



2.) Field Plots. 1981 

Seed yields from the metalaxy 1-treated plots were 
significantly less (k=100) than from the 

benomy 1-treated , benomyl plus me t a 1 axy 1- 1 r e a t ed , or the 
untreated-check plots. Yields were quite low -- about 



36 



11 quintals per hectare, or 38% of the 1980 plot seed 
yield. No yield response to applications of benomyl was 
present in other fungicide trials during that year (63, 
7 0, 87). 

Plants from block three were not similar to plants 
from the rest of the blocks. No logical explanation for 
block three being completely different from the 
remaining blocks was evident from field observation. 
The block did not have uneven stress of water, insect, 
nutritional, or disease. It is possible that block 
three accurately described the metalaxy 1-treated plants 
and the remainder of blocks did not. When the majority 
of the block by treatment interaction was removed (Table 
8), the metalaxy 1-treated plants were significantly 
(P=0.05) larger than all the rest of the plants. It is 
my feeling that the selected individual plants from the 
metalaxy 1-treated plots did not reflect the true 
population. Metalaxy 1-treated plots produced the lowest 
seed yield, but the selected plants produced the highest 
yields. With the exception of the metalaxy 1-treated 
plants, there were no significant (P=0.05) differences 
present in the entire, individually harvested plants, or 
when the plants were separated into pods, seeds, and 
stems. These results, with the exception of the 

metalaxy 1-treated plants, were similar to 1980 results 
of no significant (P=0.05) differences between plants. 



or the plant parts among treatments (Table 2). 
Possibly, the sample size (four) from each of the 1981 
season experimental plots was inadequate to describe the 
population accurately. 

Again in 1981, benomyl-treated plants, with or 
without metalaxyl, had significantly (P=0.05) fewer 
symptoms and signs of pathogens than plants which had 
not received a benomyl treatment. Seed infection levels 
were low in 1981. The extremely dry weather was 
undoubtedly the reason for the unusually healthy seed. 
The seed infection levels for the individually harvested 
plants ranged from 2% to 4% and from 1% to 7% for the 
plots (Tables 10, 11). Pathogen ratios, as occurred in 
1980 (Tables 4, 5), would not be expected to occur. 
Small changes in raw numbers drastically change ratios 
when the values are less than 5%. 

In the separate experiment, selected applications 
of benomyl improved pod and stem quality over that of 
the controls. Yields were low, and there were no 
consistant significant differences among the treatments. 
However, pod and stem quality was significantly improved 
with the application of benomyl. Applications around 
flowering (weeks 11 and 13) provided significantly 
better protection against the presence of disease 

symptoms and signs of the pathogen than application at 
planting or than no application at all. Flowering 



38 



triggers drastic physiological changes in the soybean 
plant. It is possible that the period of flowering is 
when the pathogens, already present in the soybean 
plant, proliferate throughout the stem. If this 
hypothesis is correct, it would explain why the benomyl 
treatments at 11 and 13 weeks significantly improved 
stem and pod quality. An experiment specifically 
designed to investigate the growth of systemic plant 
pathogens would have to be undertaken before any 
generalizations could be drawn. 

The block by treatment interaction present in the 
seed yields from the separate experiment can not be well 
explained. No pattern of one treatment producing higher 
seed yield was present. The conclusion from this study, 
under the conditions of that year (57-61), would be that 
benomyl treatments did not increase yield. The 
recommended benomyl treatment (2) was for applications 
to be made at 11 and 13 weeks from planting. This 
treatment did not produce increased yields. Benomyl 
applications during years unfavorable for pathogens did 
not increase seed yields (Tables 1, 6, 12), but did 
significantly improve stem and pod quality (Tables 3, 9, 
13). It would seem logical to conclude that seed 

infection would also be reduced with benomyl 

applications and result in higher stem and pod quality, 
but more disease than was present for these years would 



39 



be needed for verification. Possibly, under more 
disease than existed in 1980 and 1981, benomyl 
treatments would also improve seed yield. 



IV. DESCRIPTION OF THE MODEL 



A.) Introduction 



Growth models exist for crops (11, 14, 15, 35, 50, 
76, 88), specific aspects of crop growth (4, 16, 35, 
77), or crop - pest interactions (6, 11, 12, 17, 38, 44, 
45, 47, 48, 61, 68, 69, 74, 81, 82). The models vary in 
sophistication, accuracy, purpose, and applicability. 
Growth models for soybean exist (15, 33, 50, 84, 88), 
but applying the models to investigate disease threshold 
levels and the disease intensity - yield loss 
relationship would be outside the scope of the intended 
application of these models. Problems arise when a 
model is used for an application for which it was not 
intended . 

The soybean plant growth model developed from this 
work was specifically designed to evaluate disease 
threshold levels and the disease intensity - yield loss 
relationship . 

The ^x post facto approach was used for this model 
for a number of reasons. The model was designed to be 
exceedingly simple -- a set of regression equations. 
The approach would permit easy changes from year to year 



40 



41 



and would have wide applicability among locations. 
Regression analysis is ideally suited to the ex post 
facto approach, and large data sets can be handled 
efficiently. The presence of large data sets, along 
with the use of regression analysis (32) to construct a 
continuous function from discrete points of a 
quantitative variable, facilitated the choice of this 
mathematical technique for the model. 

A continuous mathematical function to describe the 
biological nature of the crop or crop - pest interaction 
was not considered for the basic model. Holistic 
experiments contain numerous uncontrolled and unmeasured 
variables. Water stress, nematode infestation of soil, 
soil-borne insects, and nutritional stress would be 
included in the unmeasured variables. It is imperative 
that these variables be included in a 

biological-function model. 



B.) Materials and Methods 



During 1981, whole-plant samples were collected for 
16 weeks, starting three weeks from the date of planting 
and continuing through harvest (124 days from planting) 
from a soybean field planted to breeding line F76-8846. 
The disease damage and insect damage were expressed as 
percent of total leaf area for each leaf. All 



42 



measurements were comparisons against leaf-area diagrams 
and insect- and disease-rating scales developed for 
soybeans (Appendix E). On each sampling date, the 
above-ground plant was separated into stems, leaves, 
petioles, pods, and seeds. The plant parts were dried 
and weighed. The relationship between leaf area and 
leaf dry weight was determined from these plants. A 
separate regression equation was developed for each 
dependent variable; time was used as the quantitative 
independent variable, with up to 15 degrees of freedom 
available for the regression equation. 

The equation which described the leaf dry-matter 
accumulation over time was used to drive the model. The 
separate regression equations which described dry-matter 
accumulation by pods, stems, seeds, and petioles at each 
day, were related mathematically to the equation which 
described dry-matter accumulation by leaves alone. The 
relationship was strictly a ratio. The ratios of stems 
: leaves, seeds : leaves, pods : leaves, and petioles : 
leaves were established for each day of the model. The 
equation which described leaf dry-matter accumulation 
over time produced stems, seeds, pods, and petioles, 
varing in dry-matter accumulation proportional to leaf 
dry-matter accumulation. Problems associated with 

mathematical descriptions of biological data were 

handled as they arose. Dry matter of the plant organs 



43 



was not permitted to be negative, it was set to zero 
when this condition occurred. No dry weight was 
accumulated for seven days after planting. This time 
period was reserved for seed germination and seedling 
emergence. 

C . ) Results 

The linear relationship between leaf area and leaf 
dry-matter (Fig. 1) was highly correlated (r= 0.97). 
The dry-matter accumulation of stems over time was also 
well (r=0.89) described by a straight line (Fig. 2). 
The dry-matter accumulation of seeds over time was 
described by a cubic equation with an associated r = 
0.94 (Fig. 3). The dry-matter accumulation of pods over 
time was described by a quadratic equation with an r = 
0.89 (Fig. 4). The dry-matter accumulation over time of 
petioles and leaves was described by separate cubic 
equations with r = 0.87 for petiole (Fig. 5) and r = 
0.89 for leaf dry-matter accumulation over time (Fig. 
6). The dry-matter accumulation for whole plants over 
time is presented in Fig. 7, where a linear equation 
with an r = 0.89 described the response. Values of r = 
0.48, 0.61, 0.72 corresponded to P = 0.05, 0.01, 0.001 
for 15 degrees of freedom (42). 



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DRY WEIGHT (g) 



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58 



Figures 2 through 7 represent the positive 
dry-matter accumulation over time and associated 
coefficients of determination for soybean plant organs. 
When a mathematical equation described negative 
dry-matter accumulation, the dry matter was set to zero. 
The model had a flexible harvest date, determined by the 
progression of leaf dry-matter accumulation, but 
generally was run 123 or fewer days from sowing. 



D.) Discussion 



To have a soybean model driven by leaf dry-matter 
accumulation over time seems a reasonable approach, as 
leaves produce photosynthate to fill pods and seeds and 
to grow stems and petioles. Shibles and Weber (72, 73) 
have reported soybean dry-matter accumulation was 
proportional to leaf area. The proportionality of 
dry-matter accumulation to leaf area is not unique to 
soybeans. Allen and Scott (1) described a linear 
relationship between dry-matter accumulation in potato 
and interception of solar radiation; interception of 
solar radiation was proportional to leaf area. Peanut 
canopies under various disease and insect stresses had a 
linear leaf area to leaf dry-weight relationship; LAI 
values from the work ranged from 0.32 to 3.55 (9). 



59 



The described model is not intended to be an 
all-encompassing model, as others have attempted (50). 
The regression equations are not intended to be viewed 
as stimulus - response situations. The flow chart for 
this model (Fig. 8) is simple and readily adaptable 
compared to SOYMOD (50) or SIMED (35). Changes in the 
regression coefficients, or in the regression equations, 
can describe different soybean varieties, different 
locations, or different years (Figs. 9, 10). This 
flexibility is desirable in the basic framework of a 
model . 

The time reserved for seedling emergence can be 
easily changed to more or less than seven days. This 
parameter would be location dependent. The seven days 
for seedling emergence for northern Florida climatic 
conditions was adequate. The model could be refined to 
have the time required for seedling emergence linked to 
soil temperature and moisture or planting date. This 
refinement would be better than setting the time as a 
fixed value. Plant dry weight was not permitted to be 
negative, as a negative measurement of dry weight is 
biologically meaningless. The regression equations were 
continuous with respect to the time variable. This, 
along with the equations not intending to describe a 
biologic phenomenon but to explain a phenomenon, can 
lead to negative values. Time was continuous in the 



61 




63 




65 




(6) iHOGM Ada 



66 



equations and days were integers in the model; dry 
weight can increase from 0.0 g to 0.03 g or higher in 
one day. Linear extrapolation to zero was performed on 
the four days previous to measurable dry-weight 
appearance (and disappearance) to smooth out the curve. 
The adjustment did not affect the performance of the 
model; the model-derived curves more closely represented 
the biology of the system. The model was set to run 123 
days from planting. The time duration was set by the 
equation describing the leaf dry-weight accumulation; 
the soybean fields under intensive study for two 
consecutive seasons were harvested 123 and 124 days 
after sowing. 

The correlation coefficients and the coefficients 
of determination were not 1.00 for any of the regression 
equations. The field data may be biased, which would 
produce a less-than-perf ect fit. The field data may not 
be accurate or may not accurately represent the actual 
plants, again introducing bias. Random variation, a 
common obstacle with field data, likely accounts for 
most of the variation. Had the correlation coefficients 
been less than 0.80 or the coefficients of determination 
less than 0.60, some concern as to whether- the 
regression equations accurately described the field data 
might be warranted. The data were well fitted by the 

presented equations (Figures 1 - 7). 



V. VALIDATION AND VERIFICATION OF THE MODEL 



A. ) Introduct ion 



Validation is a continuing process; a model is 
really never finished, it can always be improved, 
adjusted, and expanded. Validation is the application 
of the model to conditions different from those under 
which the model was developed. The different conditions 
can range from different plants to different hosts, 
depending on the type of model. Validation data for 
this model are from different plants of the same 
cultivar, different growing seasons, and different 
soybean cultivars. The soybean cultivar, soil type, 
general cultural practices (fertilization, insect 
control, disease control, cultivation) remained 

unchanged between the two seasons. Host growth 
differences (Figs. 9, 10; Tables 2, 7), seed yield 
differences (Tables 1, 6), differences in disease 
intensity (Tables 3, 9), and incidence (Tables 4, 5, 10, 
11) can be crudely attributed to weather conditions 
(51-61) . 

Far more validation data than appear here are 
needed to discern which specific parameter(s) are 



67 



68 



responsible for yield and disease differences. This 
type of further refinement could take many, many growing 
seasons to substantiate. 

Verification must be performed on the model. 
Verification assures the model properly mimics the 
biological system under one set of conditions, namely, 
the set of conditions under which it was developed. 
Futher model verification with published data from 
literature is useful. Often, published data are the 
sole source for physiologically-based models. Because 
of the nature of this model, published data did not aid 
in the development or adjustment of the model, they only 
verify that data used to develop and validate the model 
were similar to published data. 

B.) Materials and Methods 

The soybean growth model was validated with 200 
data sets from 1980 and 80 data sets from 1981; all 280 
data sets were independent of the data set used to 
develop the model, but were the same soybean cultivar. 
The validation data sets consisted of nondestructive 
leaf-area measurements throughout the growing season and 
final measurements of dry weight for above-ground plant 
parts. The final measurements were destructive. Leaf 
area was converted to leaf dry matter (Fig. 1). Leaf 



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ACTUAL 
DRY MATTER 

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ACTUAL DRY MATTER (g) 



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dry matter was regressed on time, with up to 15 degrees 
of freedom available for time, the independent variable. 
The resultant regression equation was used to drive the 
model (Fig. 8). The model predictions of final dry 
weight of the plant portions were compared against the 
measured data (Figs. 11-16). 

Weekly nondestructive measurements of leaf area and 
disease incidence and severity were recorded for each 
leaf of five randomly selected soybean plants, variety 
Cobb, and five randomly selected soybean plants, variety 
Bragg. The plants were grown under overhead irrigation, 
with a water management scheme directed toward zero 
water stress (L. Hammond, personal communication ). At 
maturity, the individual plants were harvested and the 
dry-weights for pods, seeds, and stems were measured. 

The accuracy of the model was verified with 
published data (43, 72, 73, 78, 79). Published 
regression parameters and agronomic values such as slope 
of dry-matter accumulation of plant portions, dry-weight 
ratios between plant portions, and dry-matter 
accumulation for the plant parts were compared against 
model predictions. 



82 



C.) Results 

Validation data of model-predicted versus actual 
yield of stems, pods, and seeds for 1980 are presented 
in Figs 11-13. Model-predicted versus actual yield for 
1981 appear in Figs. 14-16. Not all growth simulations 
were ceased on the same day. Since the plants were 
harvested at 123 or 124 days from planting, 
theoretically, the growth simulations should cease on 
the same day. 

Final plant-portion dry-weight ratios for Cobb and 
Bragg soybean varieties, grown under an intensive 
overhead irrigation scheme, are presented in Table 15. 

Generally in the literature, instead of the profile 
of an entire growing season, sections of the growth 
curve for plant organs were analyzed where their growth 
was linear. The seed dry-matter accumulation was 
analyzed after 83 days from planting, where a linear 
equation accurately fitted the data. When my data were 
treated similarly, analyzing from day 83 through day 
123, the slope coefficient for the simulated data was 
0.42, for the raw data was 0.45, and was 0.41 (Table 16) 
for the published data (28). The pod dry-matter 
accumulation over time also could be described by a 
linear equation, when 77 days from planting through 
harvest were analyzed. When my data were treated 



83 



Table 15. Harvested above-ground plant dry matter 
proportioned into seeds, stems, and pods for two soybean 
cultivars, 1981. 



z 

Soybean cultivar Plant portion (%) 

Seeds Stems Pods 



Bragg 52 33 15 

Cobb 53 34 13 



Mean of three plants. 



84 



Table 16. Published values, model predictions, and 1980 
and 1981 raw data for the same parameters. 



Whole-plant 

Parameter Published Simulated Raw 1980 1981 



z 

Seed 



dry-mat t er 
accumulation 
(g/day) 0.41-0.47 0.42 0.45 — 

Pods^ 
dry-mat t er 
accumulat ion 
(g/day) 0.19 0.11 0.11 ~ 

Seed : Pod 
dry-mat ter 

ratios 2.47-2.63:1 ~ -- 2.56:1 2.27:1 

Seed : stem 
dry-mat ter 

ratios 1.53-1.59:1 — — 1.50:1 1.66:1 



•^After day 86. 
After day 77 . 



85 



similarly (analyzing 77 days from planting through day 
123, harvest), my slope coefficient for the simulated 
and the raw data was 0.11, compared to 0.19 for the 
published value (28). The final ratio of seed:pod 
dry-matter for 1980 was 2.56:1. Published values range 
from 2.47:1 to 2.63:1 (Table 16) for commercially-grown 
soybean varieties in the United States (22, 28,); the 
1981 seed:pod dry-matter ratio was 2.27:1. The final 
ratio of seed:8tem dry-matter for 1980 was 1.50:1, 
compared to published ratios of 1.53:1 to 1.59:1 for 
commercially-grown soybean varieties (28); in 1981, the 
ratio was 1.66:1 (Table 16). The 1980 to 1981 
variations in final plant dry-matter ratios were 
reflected in the proportions of plant dry-matter ratios. 
For 1980 and 1981, 49% of the final-plant dry-matter by 
weight was seeds, 19% was pods in 1980 compared to 22% 
in 1981; 32% was stems in 1980 compared to 29% in 1981 
(Table 17). The higher percentage of pods and lower 
percentage of stems in 1981 were reflected in a lower 
seed:pod dry-matter ratio and a higher seed:stem 
dry-matter ratio. 

D.) Discussion 



The model predictions of final dry matter for seeds 
(Figs. 11, 14), pods (Figs. 12, 15), and stems (Figs. 



86 



Table 17. Harvested, above-ground, soybean plant 
dry-matter proportioned into seeds, stems, and pods for 
two seasons. 



Season 

igso^' 

1981^ 

^Mean of 200 plants, 
^ean of 80 plants. 



Plant portion (%) 

Seeds Stems Pods 

49 32 . 19 

49 29 22 



87 



13, 16) were well within a reasonable range, especially 
considering cumulative error. The leaf area to leaf 
dry-matter equation had 94% of the variation in the data 
explained by the linear relationship (Fig. 1), leaving 
6% as unexplained error. A linear relationship between 
leaf area and leaf dry matter for soybeans (39) and for 
alfalfa (64) has been published. Likewise, the stem, 
pod, and leaf dry-matter accumulation equations have 79% 
of the variation in the data explained (Figs. 2, 4, 6), 
leaving 21% of the variation as unexplained error. The 
seed dry-matter accumulation had 88% of the variation 
explained by the relationship (Fig. 3), leaving 12% as 
unexplained error. Thus the model-predicted dry-matter 
accumulation for seeds starts with 18% (6% + 12%) 
absolute error. The model-predicted pod and stem 
dry-matter accumulation starts with 27% (6% + 21%) 
absolute error, and have whatever portion of the 
unexplained error the leaf area equations contain in 
addition. Clearly, one can rapidly increase the error 
to over 100%. The error mentioned is absolute error. 
The 27% error could be as low as 15% if the 6% leaf area 
to leaf dry-matter accumulation error cancels a portion 
of the 21% error of the pod or stem dry-matter 
accumulation. The leaf area equation (Figs. 9, 10) 

added to this could possibly cancel the 21% completely, 
or add completely to it. Obviously, the cumulative 



88 



error must be followed, but no cause - effect 
relationship can be assessed to the cumulative error. 

The final plant-portion dry-matter ratios from Cobb 
and Bragg soybean cultivars (Table 16) were similar to 
the ratios obtained from plants used in model 
verification (Table 17). In view of this evidence, 
using ratios, updated daily, to produce growth of stems, 
seeds, and pods in the model is a reasonable approach. 
The seed:stem dry-matter ratio for Cobb and Bragg 
soybean cultivars (Table 18) was very similar to the 
ratios obtained for a different soybean cultivar grown 
under different conditions (Table 16), and also similar 
to published ratios of totally different soybean 
cultivars (28). The seed:pod ratio from the Cobb and 
Bragg soybean cultivars (Table 18) were much higher than 
published ratios (22, 28) or the ratios from another 
soybean cultivar (Table 16) grown under a less intensive 
water management scheme. Again, the pod dry-matter 
accumulation, as discussed earlier, may be more 
sensitive to environmental changes, which includes 

water, than are the seeds or the stems. 

The slopes from the linear equations describing the 
seed dry-matter accumulated (Table 16) for the 
simulation and published data (28) were similar (0.42, 
0.41), but the Y-intercepts were not (0.43, 1.61). I 
interpret the Y-intercept to be location dependent. 



89 



Table 18. Final plant dry-matter ratios for two soybean 
cultivars , 1 981 . 



Dry-matter rato Soybean cultivar^ 



Bragg Cobb 



Seed:stem 1.57:1 1.59:1 

Seed:pod 3.43:1 4.08:1 



Mean of three plants. 



90 



variety dependent, maturity group dependent, or perhaps, 
planting date dependent. The slope, in grams per day 
(after day 86), of the seed dry-matter accumulation is 
not influenced by, for instance, location, as much as 
the Y-intercept. Soybeans grown in northern United 
States would not be expected to initiate seed dry-matter 
accumulation, measured by the Y-intercept, at the same 
time as soybeans grown in the southern United States. 
Similarly, soybeans planted in northern United States 
are included in different maturity groups than those 
planted in the southern United States. The soybeans 
included in northern maturity groups would be expected 
to initiate dry-matter accumulation at a different 
number of days from planting, which is again measured by 
the Y-intercept. Weather would also be expected to 
influence the seed dry-matter accumulation. I 
hypothesize that weather would influence the slope of 
the dry-matter accumulation curve, but not the 
Y-intercept. Only drastic differences in 

photosynthetical ly-act ive radiation, water availability, 
or ambient temperatures would cause notable changes in 
the seed dry-matter accumulation slope to occur. The 
linear relationship between seed dry-matter acculumation 
and time (after flowering) has also been previously 
reported (8, 20, 21). 



The slope of pod dry-matter accumulation from this 
study (0.11) was different from a previously 
reported(28) slope of pod dry-matter accumulation 
(0.19). My Y-intercept (-1.21) for pod dry-matter 
accumulation was also different from the value I 
calculated from published data (-2.92). I expect pod 
dry-matter accumulation to be influenced by the same 
factors that influenced seed dry-matter accumulation. 
Had Hanway and Weber (28) included data points between 
70 and 75 days from planting in regression (which were 
omitted), the slope for the pod dry-matter accumulation 
curve would be lower than 0.19, perhaps even close to 
0.11. The Y-intercepts also may be closer to the ones I 
found with these data points included in the regression. 
The coefficient of determination for the published pod 
dry-matter accumulation curve (28) is 0.92; including 
data points between days 70 and 75 in the regression 
analysis will lower the coefficient of determination as 
well as the slope. Any damage to the pods by insects or 
plant pathogens, which was present in published studies 
and was different from what was experienced in my 
studies, would account for some variation between the 
slopes of the pod dry-matter accumulation. 



92 



The linearity of the entire plant (sum. Fig. 7) has 
also been previously reported (31, 71). 

The 1980 final dry-matter ratio for seeds:pods was 
similar to published values (28), but the 1981 ratio was 
not. Weather conditions much different from those in 
1980 prevailed during 1981; there was more water and 
more pho t osynthe t ica 1 ly-ac t ive radiation during the 1980 
growing season than during the 1981 growing season 
(51-61). The environmental conditions which prevailed 
in 1980 more closely resembled the long-time averages 
than did the 1981 environmental conditions. The 1980 
environmental conditions can then be considered more 
"normal," or closer to "normal" than the 1981 
environmental conditions. As expected in the seed:stem 
dry-matter ratios, the 1980 data were similar to 
published data, and the 1981 data are not. Again, 
environmental conditions were different during the two 
seasons. 

The seeds were the same proportion of the total dry 
matter for both the 1980 and the 1981 growing season. 
This further supports the rate of seed dry-matter 
accumulation being relatively unaffected by 

environmental changes. Had the 1980 to 1981 

environmental variation been more pronounced, the seed 
proportion of the final plant dry-matter ratio may have 
been lower. Environmental conditions responsible for 



lowering the seed proportion of final plant dry-matter 
ratio would probably also cause the slope of the seed 
dry-matter accumulation curve (from day 86 through 
harvest) to be different from 0.42. The increased 
proportion of final ratio of pods during 1981 (3% 
increase over 1980) may account for the decreased slope 
and increased Y-intercepts over the published values. 



VI. APPLICATIONS FOR THE MODEL 



A.) Introduction 



A model is a simplified representation of a system. 
The primary purpose of a model is to be applied to the 
system, to interpret changes, be they growth, abiotic 
pressures, or biotic pressures. The models can then be 
used to predict the effect these changes have on final 
plant yield or final level of biotic stress. The actual 
parameters that are predicted, and the influences which 
these parameters have on the system, are dependent on 
the intended purpose of the model, the choice of 
modeling techniques, and the individual modeler. The 
intended purpose of this model of soybean growth was to 
be a disease intensity - yield loss model. The ex post 
facto approach coupled with regression analysis was 
ideally suited to the development of a basic model. The 
separation of plants into pods, stems, seeds, and leaves 
permitted the flexibility for stress to be directed to 
individual plant portions, or to the entire plant. The 
accumulation of leaf dry-matter (related to leaf area. 
Fig. 1) drove the model, with the reasoning that the 
leaves supply the plant with carbohydrates. The 



94 



95 



reduction in leaf area by foliar diseases or by 
foliage-feeding insects (velvetbean ca t t erp i 1 lar , 

Ant icar s ia gemmatal is Hubner; green cloverworm, 
Plathypena scabra Fabr icus ; soybean looper, Ps eudop lus ia 
includens Walker) would result in directly reducing the 
production of carbohydrates. The reduction in 

carbohydrate production would cause reduction in 
dry-matter accumulation in stems, seeds, and pods. The 
southern green stink bug ( Nezara viridula L.) pierces 
the soybean pod and destroys the seed within the pod. A 
specific equation to reduce the seed dry-matter 
accumulation dependent upon the numbers of southern 
green stink bugs could be developed and incorporated 
into the seed dry-matter accumulation equation. 
Subtraction of damaged seed would give a resultant yield 
loss associated with various levels of this insect. 
Similarly, the intensity of non-foliar diseases such as 
pod and stem blight or anthracnose could be incorporated 
into the model. The disruption of the normal growth 
process in the stem, pod, and seed could be quantitated, 
and the resultant terms introduced into the equations 
for dry-matter accumulation. Mathematical separation of 
dry-matter accumulation for seeds, stems, and pods 
permitted each individual plant portion to be weighted 
differently for pathogen intensity - yield loss 
reduction. Undoubtedly, different equations would be 



96 



involved for stems, seeds, and pods. The pathogen 

intensity in the seeds could be related to the intensity 

in the pods, which could, in turn, be related to the 

intensity in the stems. The following uses for the 
soybean growth model are strictly hypothetical; 

pertinent data to verify the uses have not been 
collected. 

B.) Uses For The Model 

1.) Foliar Pathogens 

In the model, attack by foliar pathogens can be 
simulated by removal of leaf area representative of 
pathogen damage. Since leaf area drives the model, 

reduced leaf area would cause a reduction in dry-matter 
accumulation by seeds, stems, and pods. However, the 
intensities of the foliar disease in my field 
experiments over two years did not cause a decrease in 
dry-matter accumulation. The presence of low levels of 
disease without a statistically significant change in 
yield is an example of a disease threshold phenomenon: 
a specific level of disease must be present before a 
significant change in yield is detected. The influence 
of foliar diseases on yield could be modeled several 
ways. The disease progress data could be to fit to a 



97 



continuous curve and the equation which described the 
curve used to remove leaf area from a leaf-area 
equation. A second approach would be to use the 
equation which described the disease progress data to 
add dry matter to the leaves. This addition would 
permit calculation of the theoretical yield which may 
have been produced in the absence of disease. Downy 
mildew disease progress data from 1980 (37) were fitted 
to a Gompertz (3) equation (Eq. 1) 



Yi= Ymax * exp(-b * exp{-k * T}) (Eq. 1) 

Yi= disease at time i 
Ymax= maximum disease 
Yo= initial disease 
b= -ln(Yo) 
k= epidemic rate 
T= time 



using a nonlinear, least-squares estimation routine. 
Epidemic rates (k) were solved with initial disease and 
maximum disease severity held constant (Yo = . 00001 ; 
Ymax=0.02) {37}. The Gompertz equation was used to 
mathematically remove leaf area from the plant. No 
major differences in dry-matter accumulation from the 
low disease levels occurred. Foliar diseases other than 
downy mildew could also be handled in this way. A large 
range of disease intensities, including intensities 
higher than measured in this study, are necessary to 



verify and validate this approach to describe the 
disease severity - yield loss relationships. The 
incorporation of equation 1 into the flow chart of the 
soybean growth model is presented in Fig. 17. 

2.) Above-ground Non-foliar Pathogens 

Pod and stem blight and anthracnose did not 

regularly appear on the leaves, so reduction of 

dry-matter accumulation by these pathogens must be 

accomplished in a way other than removing leaf area, the 
driving force of the model. One approach would be to 

remove dry-matter accumulation of the stems, pods, and 

seeds, dependent upon a visual rating of the stems and 

pods at harvest. Equation 2 would accomplish the dry 
matter reduction. 

R= (S**3 / Smax**3) * M (Eq. 2) 

R= percent reduction in plant 

dry-matter accumulation 
S= average of stem and pod rating 

(based on percent infection) at harvest 
M= maximum percent dry-matter reduction 

by pod and stem blight and anthracnose 
Smax= maxmium pod and stem rating possible 

Reduction in dry-matter accumulation (R) would be 
removed from the seed, stem, and pod weight. The cube 



100 




LEAF AREA 



vAAA/aaa 



<?<■ 



FOLIAR 
PATHOGENS 



LEAF AREA 



101 



of the mean plant quality rating (S) divided by the cube 
of the maximum quality rating is suggested to mimic 
observations from the field that the most infected 
plants in a field yield the smallest amount of dry 
matter, but not on a 1:1 relationship between percent 
infection and reduced yield. Often, moderately infected 
stems and pods yield as much plant dry matter as clean, 
or nearly clean plants. The cubic-shaped curve would 
remove proportionally more dry-matter accumulation at 
higher percent disease than at lower percent disease. 
The maximum percent dry-matter reduction (M) 
attributable to pod and stem blight and anthracnose 
would have to be determined experimentally. The purpose 
of {M} is to place a limit on the amount of damage 
attributable to these type of plant pathogens. It is 
very possible that {M} may change from season to season, 
dependent upon the environmental conditions. A 
reasonable value for {M} would be <15%. 

Similarly, seed quality (as opposed to quantity, 
above) could be evaluated with equation 3. 

Q= (S**2 / Smax**2) * P (Eq. 3) 

Q= reduction in seed quality 

S= average of stem and pod rating (based 

on percent infection) at harvest 
P= maximum quality reduction by pod 

and stem blight and anthracnose 
Smax= maximum pod and stem rating possible 



102 



The reduction in seed quality (Q) could be a measure of 
field emergence, or a combination of germination before 
and after accelerated aging, field emergence, and 
electrolyte conductivity. For these purposes, the 
specific measure of seed quality is unimportant. The 
squared mean plant quality rating (S) divided by the 
maximum quality rating squared would likely yield a more 
representative curve than a cubic equation for seed 
quality. Any infection, evidenced by symptoms and signs 
of pathogens on the stems and pods, could lead to seed 
infection and reduced seed quality. However, the higher 
the disease severity, the better the chance that the 
infection has reached the seed, hence the squared term 
to describe this type of relationship. The maximum 
quality reduction (P) would have to be determined 
experimentally. By whatever means used to measure {Q}, 
be it on a scale of to 25 or in percent, {P} would 
also have to appear in the same form. An appropriate 
value for {P} could be 100% of the scale used, with a 
much delayed harvest. Under normal conditions, 20% of 
the range of the {S} scale would be a reasonable value. 
The seed quality reduction from infection by pathogens 
would be difficult to separate from physiological 
decline, and both may have to be included in equation 3. 
A factor multipled by the number of days after the 



103 



optimal ripeness that the seed was harvested would 
accommodate this change. The incorporation of equations 
2 and 3 into the flow chart of the soybean growth model 
is presented in Fig. 18. 

3.) Soil-borne Pathogens 

Yield losses caused by soil-borne pathogens would 
be difficult to incorporate into the present form of the 
model. Soybean root growth was not used in the model. 

Yield losses resulting from soil-borne diseases need to 
be handled with a root submodel. A model of fusarium 
root rot has been published (6, 7); a similar model 
would be adequate for soybeans. In keeping with the ex 
post facto nature of the model, root growth could be 
incorporated in a way similar to the stems, pods, seeds, 
and petioles. Ratios from published curves of 

dry-matter accumulation for roots and leaves (50) could 
be extracted, and added to the existing model. The root 
model would not alone explain the pathogen - root 
system. In continuing this approach, attack by 

soil-borne pathogens could be simulated using the Monte 
Carlo method (27). Severe attack by, for instance, 

Fusarium spp., kills the individual plant. The 
percentage of plant-kill, determined by the severity of 
soil infestation by Fusarium spp., could be removed from 



ABOVE-GROUND 
NON-FOLIAR 
PATHOGENS 




DRY MATTER 



106 



yields of a specified area. The soybean plant has the 
ability to compensate for missing plants to a certain 
degree (78). Compensation by the soybean plant would 
have to be investigated more intensely and inoculum 
density - disease intensity relationships defined for an 
accurate description of loss attributable to soil-borne 
pathogens to become a reality. The incorporation of 
soil-borne diseases into the flow chart of the soybean 
growth model is presented in Fig 19. 

4.) Foliage-feeding Insects 

A comprehensive model which mathematically 
describes the growth of the velvetbean caterpillar has 
been developed and validated (48, 49). This latter 
model, as any model, is not perfect, and has limitations 
as noted (48). The model could be directly linked to 
the soybean growth model to remove leaf area consumed by 
velvetbean caterpillar. An aspect not incorporated into 
the velvetbean caterpillar model (48) was insecticide 
application, which could be easily handled by 
introduction of an insect mortality factor for each 
chemical used. The incorporation of foliage-feeding 
insects into the flow chart of the soybean growth model 
is presented in Fig. 20. 



108 



SOIL-BORNE 
PATHOGENS 




INSECTICIDE 



vAAAaaaa 



LEAF AREA 



Q4- 



nAAAAAAA 



OLIAGE-FEEOtNO 
INSECTS 




(^^-OSSy (^^LOSS^ 
LEAF AREA INSECT MORTALITY 



Ill 



5«) Seed-feeding Insects 

Southern green stink bug is the major seed-feeding 
insect which annually causes damage in soybean fields. 
Damage this pest causes could be described by equation 
4. 



D= I * (T-86) * 0.42 * Y * Z + 0.43 (Eq. 4) 

D= reduced dry-matter accumulation by the seed 

1= insect population per area 

T= time from planting in days 

Y= number of seed destroyed per insect per day 

Z= dry-matter weight of one seed 



Reduced dry-matter accumulation by seed (D) is removed 
directly from the accumulation of any one day after 86 
days from planting. Eighty-six days from planting is 
suggested as a reference point because this is where a 
published linear equation describes the seed dry-matter 
accumulation (28), and my data (Table 15) are in good 
accord. Appearing in equation 4 is a 0.42 factor which 
describes the plant dry-matter accumulation per day for 
the seeds after 86 days from planting, in grams. Also 
appearing in equation 4 is a 0.43 factor added for the 
Y-intercept value, in grams, from the linear equation 
(Table 15). The number of seed destroyed per insect per 
day (Y) would have to be determined experimentally. The 



112 



dry-matter weight of one seed (Z) is important to keep 
the equation to scale. The scaling permits removal of 
numbers of destroyed seed, not percentage of destroyed 
seed. The insect population per area (I) can be 
determined from "row shakes" — a standard sampling 
procedure for entomological studies. However, the 
equation is such that the insect population would have 
to be updated daily after day 86 from planting. Daily 
sampling to describe southern green stink bug 
populations is not practical, so an approach similar to 
the velvetbean caterpillar model (48) could be used. 
The insect growth from egg through instars to adult is 
modeled for velvetbean caterpillar (48). The same 
model, with modifications, could be used to update daily 
the southern green stink bug populations. Modifications 
would be necessary for differences in growth stages and 
respective damage caused by each growth stage between 
southern green stink bug and velvetbean caterpillar. 
Again, an insecticide submodel could be easily 
incorporated. The incorporation of equation 4 into the 
flow chart of the soybean growth model is presented in 
Fig. 21. 




DRY MATTER 



INSECT 
MORTALITY 



115 



C.) Results 

Results are described only for those applications 
which appropriate data were collected from my field 
experiments. Data on foliar diseases and above-ground 
non-foliar plant pathogens (causing quantity and quality 
losses) are included in this section. 

Foliar disease levels were very low throughout the 
1980 and the 1981 growing seasons. Equation 1 
parameters of Ymax=0.02, Yo=0. 00001, and k=0.0294 
(actual data {37}), decreased the dry-matter 
accumulation for seeds, stems, pods, petioles, or leaves 
only 0.07%. When the plant seed yield was converted to 
bushels per acre, there was no significant difference at 
the 5% level. The maximum disease severity was 2%, and 
yield differences were nonsignificant at the 5% level 
(37). A threshold level of disease was not taken into 
consideration per s e in equation 1, but low levels of 
disease do not reflect a change in the final yield. If 
all the disease parameters (Ymax, Yo , and k) were 
increased, more seed yield loss would occur, which would 
simulate a more severe epidemic. An adequate range of 
disease severities would have to be present to calibrate 
the foliar-disease submodel. 

Dry matter is removed proportional to the cube of 
the quality rating of the stems and pods (Eq. 2). When 



116 



rated on a scale of to 10, the 1980 growing season 
mean pod and stem quality rating was 3 for 
benomy 1-treated plots, and 8 (roundoff error present) 
for non-benomy 1-treated plots (Table 3). If the maximum 
dry-matter loss to above-ground, non-foliar, plant 
pathogens was 15%, there would be 7.3% more seed yield 
lost in the non-benomy 1-treated plots when compared to 
benomyl-treated plots (7.3% = {1 - ({3**3 / 10**3} * 
.15)} - {1 - ({8**3 / 10**3} * .15)}, roundoff error 
present). Similarily, in 1981, there would be a 2% 
increase in yield with mean pod and stem quality ratings 
of 1 and 5 for benomyl-treated and non-benomy 1-treated 
plots (Table 9). 

The quality of seed harvested is reduced, 
proportional to the square of the mean pod and stem 
quality rating (Eq. 3). Primarily, {Q} is a measure of 
how appropriate the harvested seed would be for 
planting. If the maximum quality loss from 

above-ground, non-foliar, plant pathogens would be 20%, 
there was a 11% reduction in quality in 1980 (11% = {1 - 
({3**2 / 10**2} * .20)} - {1 - ({8**2 / 10**2} * .20)}). 
Mean pod and stem ratings of 1 for benomyl-treated and 5 
for non-benomyl-treated plots in 1981 (Table 9) would 
reflect a 5% reduction in quality from the 
non-benomyl-treated plots when compared to the 

benomyl-treated plots. 



117 



D.) Discussion 

Any of the proposed submodels can be linked to the 
soybean growth model mathematically by either 
subtraction or multiplication. This ease of application 
makes the model and submodels attractive for a basic 
framework. The disease severity was not high enough to 
test the foliar disease submodel or to determine injury 
threshold levels. There are published reports involving 
defoliation levels and yield reductions (9, 13, 17, 25, 
26, 34, 36, 37, 40, 41, 43, 46, 80, 81, 85, 86). Many 
of the reports involve non-pathogen- induced defoliation 
and consequent yield reduction (9, 25, 26, 34, 46, 80, 
81). The studies on defoliation and yield reduction 
lacked adequate spacing of treatments and lacked 
accurate quantitation of defoliation levels (9, 25, 26, 
34, 46, 80, 81). The unfortunate results of these 
shortcomings in experimental design are that these data 
cannot be used to test or modify the foliar disease 
submodel, and the threshold level for 

defoliation-induced loss cannot be extracted. As 

previously mentioned, the threshold level for loss from 
disease cannot be extracted from my work, owing to 
inadequate disease. As previously mentioned, a 

threshold level for disease has not been considered in 



118 



equation 1. Incorporation of the disease threshold in 
Fig. 17 would consist of: a value, where below, say 4% 
disease, no yield loss would occur. The rate of yield 
loss from disease may change with the amount of disease 
present. For instance: yield loss may occur at rate 
"a" for each increase in from 4-9%; from 10-15% disease, 
yield loss may occur at rate "b" for each increase in 
disease; from 16-24% disease, yield loss may occur at 
rate "c" for each increase in disease; and above 25% 
disease, yield loss may occur at rate "d" for each 
increase in disease. In addition to the disease 

threshold, there is an economic threshold (30), where 
disease control would become economically feasible. 
Detailed discussion of economic thresholds is beyond the 
intended scope of my work, but one can easily see where 
this concept could be incorporated into the foliar 
disease submodel. A mortality factor for the pathogen, 
specific for the type of control agent used would be 
assigned, as well as a cost factor; when the cost of 
yield reduction (at any assigned market price) is higher 
than the cost of the control measure, then control is 
economically feasible. 

Another problem exists in the artificial 
defoliation studies. There is no reasonable method for 
extrapolating yield reduction caused by 

no n-pa t hog en- indu ce d defoliation ( in s e c t - indu ce d , 



119 



artificial, herbivor-gr az ing , etc.) to yield reduction 
caused by pathogen-induced defoliation. The yield loss 
caused by plant pathogens is greater than the loss 
casued by reduction in leaf area equivalent to that 
which the pathogen has destroyed. The plant pathogen is 
a living system, and is invading another living system, 
the plant. In destroying all or portions of a plant the 
pathogen kills plant cells and consumes their contents. 
The action of plant invasion by the pathogen can upset 
the host physiology in localized areas, and possibily in 
the entire host. This disruption of physiological 
processes causes more yield loss than vould the 
disruption of processes which would occur from manually 
defoliating a plant at the same level of foliage loss. 

The low levels of disease present for my studies 
did not permit evaluation of the above-ground, 
non-foliar plant pathogen submodel. Statistical tests 
at the 5% level can measure the 7.6% difference in seed 
yield (Eq. 2). No significant differences as a result 
of benomyl applications were described using a 
Duncan-Waller k-ratio test (k-100) {Table 1}; however, 
Duncan's new multiple range test, a less conservative 
test, did separate seed yield into significantly 
different classes. The more conservative test was 
chosen (Table 1) because of the variation inherent in 
field situations. Environmental conditions differed 



120. 



between 1980 and 1981, which, in part, would explain why 
in 1981, there was only a 2% difference (Eq. 2) in seed 
yield. A statistical analysis at the 5% level would not 
describe a difference between two numbers 2% apart under 
these circumstances. The reduction in quality of seed 
as a result of above-ground, non-foliar, plant pathogens 
cannot be evaluated with my data. As previously 
mentioned, pathogen activity would only be partially 
responsible for seed quality reduction. A major portion 
of the reduction in quality would be physiological 
deterioration, partially or wholly independent of seed 
pathogens . 



VII. SUMMARY 



The soybean growth model described herein allows 
the researcher to test yields of soybean plants of 
different sizes and growth habits, and plant response to 
change in populations of pathogens. The model is 
intended to be a basic framework for the development of 
disease intensity - yield loss relationships for 
soybean . 

The soybean growth model was developed from two 
years of intensive field studies. Included in the 
experiments was a fungicide with specific action toward 
one soybean pathogen and a second fungicide with 
activity against most other soybean pathogens. 

Validation of the soybean growth model was 
performed with data sets independent of the set used to 
develop the model. Results published by other workers 
were used to verify the model. 

The presented soybean growth model and the disease 
intensity - yield loss submodels are to be considered as 
initial models. The development of simplistic, but 

accurate, models is always a first step in simulating a 
phenomenon as complex as plant - pathogen interactions. 



121 



122 



The description and modeling of the physiology and 
biochemistry of disease development, plant development, 
and disease - plant interactions must start with 
holistic experiments designed to determine the responses 
of these interactions. I consider the models for 
soybean growth and for disease intensity - yield loss as 
an initial step toward the development of more accurate 
models. It is work on these models which I have 
inititaed and have described here. 



APPENDIX A 
FUNGICIDES USED 



Fungicide 



Tradename, source, chemical name 
percent active ingredient 



Benomy 1 



Benlate SOW, Biochemicals Department, E, 
I. duPont De Nemours and Company, Inc., 
Wilmington, Delaware 19898. Methyl 1- 
( buty Icarbamoy 1 )-2-benzimidazole 
carbamate, 5C% a.i. 



Metalaxy 1 



Ridomil 2E, Agricultural Division, CIBA- 
GEIGY Corporation, Greensboro, North 
Carolina 27409, N- ( 2 , 6-d ime thy Ipheny 1 ) -N- 
(me thoxy ace ty 1 ) alanine methyl ester 
25.11% a.i. 



123 



APPENDIX B 
EQUIPMENT USED 



Equipment Source 

Hand-held, Weed Systems, Gainesville, Florida 32604 

two-row , 
six-nozz le , 
boom sprayer 



124 



APPENDIX C 

LEAF-AREA DIAGRAMS, INSECT- AND DISEASE-RATING SCALES 



(A-Z) 



1% A) 




126 



127 




128 




15% G) 



20% H) 



60% K) 



131 




16cm^ 0) 



30cm^ 

p) 



133 




1 15cm 

R) 



135 




T) 

158cm^ 

(T-U) 



137 




138 




139 




X) 

225cm^ (x-z) 




141 




APPENDIX D 
MATERIALS USED 



Material Source, 



Acidif ied 
potato 

dextrose agar water, 1 1 
Clorox 



Potatoes, 200 g; dextrose, 20 g; agar, 
20 g; 50% lactic acid, 3 ml; deionized 



The Clorox Company, Oakland, California 
94612, 5.25% sodium hypochlorite 



142 



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V 



f 



BIOGRAPHICAL SKETCH 



Steven Boyd Johnson was born on 30 October 1955 in 
Spokane, Washington. He graduated from West High School 
in Madison, Wisconsin, in June of 1973. In September of 
1973, he entered the University of Wisconsin-Madison; be 
received a Bachelor of Science degree in plant pathology 
in December of 1977. In January of 1978, he enrolled in 
the Graduate School at the University of Maine at Orono 
and accepted a position as a research assistant in the 
Department of Botany and Plant Pathology. He received a 
Master of Science degree in botany and plant pathology 
from the University of Maine at Orono in December of 
1979. Also in December of 1979, he married the former 
Jennifer Alice Wilcox. In January of 1980, he enrolled 
in the Graduate School at the University of Florida and 
accepted a position as a research assistant in the 
Department of Plant Pathology. On 20 April 1982, he 
became a proud, first-time father of six pouund, eight 
ounce Julie Renee Johnson. He is a member of The 
American Phy t opa the 1 eg i ca 1 Society and the Southern 
Division of that society, The International Society of 
Plant Protection, The Biometric Society and the Eastern 
North American Region of that society, Florida State 



151 



Horticultural Society, Soil and Crop Science Society of 
Florida, Alpha Zeta Agricultural Honor Society, and 
Gamma Sigma Delta Agricultural Honor Society. He is a 
candidate for the degree of Doctor of Philosophy in 
plant pathology at the University of Florida. 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and 
quality, as a dissertation for the degree of Doctor of 
Philosophy . 

Richard D. Berger, Chairman 
Professor of Plant Pathology 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and 
quality, as a dissertation for the degree of Doctor of 
Philosophy . 



2 % 



T. Edward Freeman 

Professor of Plant Pathology 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and 
quality, as a dissertation for the degree of Doctor of 
Philosophy . 




Professor of Plant Pathology 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and 
quality, as a dissertation for the degree of Doctor of 
Philosophy . 



Sherlie H. West 
Professor of Agronomy 



This dissertation was submitted to the Graduate Faculty 
of the College of Agriculture and to the Graduate 
Council, and was accepted as partial fulfillment of the 
requirements for the degree of Doctor of Philosophy. 

December, 1982 




Dean, Graduate School